Use bst_float consistently throughout (#1824)

* Fix various typos

* Add override to functions that are overridden

gcc gives warnings about functions that are being overridden by not
being marked as oveirridden. This fixes it.

* Use bst_float consistently

Use bst_float for all the variables that involve weight,
leaf value, gradient, hessian, gain, loss_chg, predictions,
base_margin, feature values.

In some cases, when due to additions and so on the value can
take a larger value, double is used.

This ensures that type conversions are minimal and reduces loss of
precision.

include/xgboost/learner.h

@@ -106,7 +106,7 @@ class Learner : public rabit::Serializable {
    */
   virtual void Predict(DMatrix* data,
                        bool output_margin,
-                       std::vector<float> *out_preds,
+                       std::vector<bst_float> *out_preds,
                        unsigned ntree_limit = 0,
                        bool pred_leaf = false) const = 0;
   /*!
@@ -162,7 +162,7 @@ class Learner : public rabit::Serializable {
    */
   inline void Predict(const SparseBatch::Inst &inst,
                       bool output_margin,
-                      std::vector<float> *out_preds,
+                      std::vector<bst_float> *out_preds,
                       unsigned ntree_limit = 0) const;
   /*!
    * \brief Create a new instance of learner.
@@ -185,7 +185,7 @@ class Learner : public rabit::Serializable {
 // implementation of inline functions.
 inline void Learner::Predict(const SparseBatch::Inst& inst,
                              bool output_margin,
-                             std::vector<float>* out_preds,
+                             std::vector<bst_float>* out_preds,
                              unsigned ntree_limit) const {
   gbm_->Predict(inst, out_preds, ntree_limit);
   if (out_preds->size() == 1) {